1d:T7d5,# Predict method for 'misrepEM' Model Fits Predicted values based on a fitted 'misrepEM' model object. ```r ## S3 method for class 'misrepEM' predict(object, newdata, ...) ``` ## Arguments - `object`: a fit from one of `gammaRegMisrepEM`, `LnRegMisrepEM`, `NormRegMisrepEM`, `nbRegMisrepEM`, or `poisRegMisrepEM`. - `newdata`: a data frame containing predictors that are to be used to make predictions of the response. - ``...``: currently not used. ## Details Currently, only predictions made on the scale of the response variable are supported. Incomplete cases are automatically dropped, and predictions are made only on complete cases. ## Returns `predict.misrepEM` returns a numeric vector of predictions. ## References Xia, Michelle, Rexford Akakpo, and Matthew Albaugh. "Maximum Likelihood Approaches to Misrepresentation Models in GLM ratemaking: Model Comparisons." **Variance** 16.1 (2023). Akakpo, R. M., Xia, M., & Polansky, A. M. (2019). Frequentist inference in insurance ratemaking models adjusting for misrepresentation. **ASTIN Bulletin: The Journal of the IAA, 49**(1), 117-146. Xia, M., Hua, L., & Vadnais, G. (2018). Embedded predictive analysis of misrepresentation risk in GLM ratemaking models. **Variance, 12**(1), 39-58. ## Examples ```r # Simulate data n <- 2000 p0 <- 0.25 X1 <- rbinom(n, 1, 0.4) X2 <- rnorm(n, 0, 1) X3 <- rbeta(n, 2, 1) theta0 <- 0.3 V <- rbinom(n,1,theta0) V_star <- V V_star[V==1] <- rbinom(sum(V==1),1,1-p0) a0 <- 1 a1 <- 2 a2 <- 0 a3 <- 4 a4 <- 2 mu <- exp(a0 + a1*X1 + a2*X2 + a3*X3 + a4*V) phi <- 0.2 alpha0 <- 1/phi beta <- 1/mu/phi Y <- rgamma(n, alpha0, beta) data <- data.frame(Y = Y, X1 = X1, X2 = X2, X3 = X3, V_star = V_star) # Split data into training and testing sets train <- data[1:1800,] test <- data[1801:2000,] gamma_fit <- gammaRegMisrepEM(formula = Y ~ X1 + X2 + X3 + V_star, v_star = "V_star", data = train) # Predict on test set; preds <- predict(gamma_fit, newdata = test) ```

predict.misrepEM function